CN113592133A - Energy hub optimal configuration method and system - Google Patents
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Abstract
The invention provides an energy hub optimal configuration method, which belongs to a data processing system special for the purpose of resource management and comprises the following steps: establishing a scene: establishing a source-load bilateral typical day scene based on historical data of a power source and a load; modeling: adding an objective function and constraint conditions to establish an EH planning configuration optimization double-layer model based on a source-load bilateral typical daily scene; solving: and solving the EH planning configuration optimization double-layer model by using a CPLEX solver to obtain an optimal configuration result and executing the optimal configuration result. The invention also provides an energy junction optimization configuration system which comprises a memory and a processor. The invention can fully consider the influence problem of uncertainty of comprehensive demand response (IDR) at the user side and solve the problem of random response of comprehensive demand; the probability density function can be directly fitted according to historical data without depending on a parameter distribution form assumed in advance, and compared with the traditional parameter estimation, the probability density function is more applicable and can give consideration to source-load bilateral seasonal correlation.
Description
Technical Field
The invention relates to an energy hub optimal configuration method and system, belonging to a data processing system special for resource management.
Background
Along with the worsening of the environmental pollution problem and the shortage of fossil fuels, the improvement of the comprehensive utilization efficiency of various types of energy and the reduction of the emission of pollutants become key problems which need to be solved for constructing a clean, low-carbon, safe and efficient modern energy system in China. The renewable energy is integrated into a comprehensive energy system with complementary energy, and the comprehensive energy system is an effective solution for dealing with the randomness of output. As an abstract mathematical model of an integrated Energy system, an Energy Hub (EH) is defined as a framework for producing, converting, storing and consuming different Energy streams, and the diversity of Energy generation modes and flexibility of consumption forms inside the EH make strong uncertainties exist on both the EH source side and the demand side.
The Integrated Demand Response (IDR) expands the traditional single-form power Demand Response to a multi-energy system, and can guide users to participate in 'peak shaving and valley filling' of a power grid more flexibly by 'multi-energy complementation'. The existing research ignores the influence of IDR uncertainty at a user side when focusing on source-load bilateral uncertainty problems in a multi-energy complementary system, and a mature IDR random response analysis mechanism is not established yet.
Disclosure of Invention
In order to solve the technical problems, the invention provides an energy hub optimization configuration method which can fully consider the influence problem of uncertainty of comprehensive demand response (IDR) at a user side, solve the problem of random response of comprehensive demand, does not depend on a parameter distribution form assumed in advance, can directly fit a probability density function according to historical data, has higher applicability compared with the traditional parameter estimation, and can consider seasonal relevance at both sides of source load.
The invention is realized by the following technical scheme.
The invention provides an energy hub optimal configuration method, which comprises the following steps:
establishing a scene: establishing a source-load bilateral typical day scene based on historical data of a power source and a load;
modeling: adding an objective function and constraint conditions to establish an EH planning configuration optimization double-layer model based on a source-load bilateral typical daily scene;
solving: and solving the EH planning configuration optimization double-layer model by using a CPLEX solver to obtain an optimal configuration result and executing the optimal configuration result.
The historical data of the power source and the power load are wind speed data and power load data classified according to seasons.
The source-load bilateral typical daily scene is established by adopting the following steps:
establishing a function: establishing a wind speed-load joint probability distribution function in each time period based on a Copula method;
secondly, scene reduction: firstly, sampling a wind speed-load joint probability distribution function, carrying out inverse transformation on a sampling result to obtain a simulation data sequence with correlation, then carrying out inverse transformation to generate a scene tree, and carrying out scene reduction by adopting a rapid forward generation method to obtain a source-load bilateral typical daily scene.
In the step (r), a Frank Copula function is adopted as the Copula function.
The sampling of the wind speed-load joint probability distribution function is realized by adopting an Archimedes Copula function sampling method.
The objective function aims to minimize annual comprehensive operation cost, wherein the cost comprises annual installation cost CIN, operation maintenance cost COM, energy consumption cost CEC and emission cost CEM.
The constraint conditions comprise power balance constraint, purchased energy constraint, conventional equipment operation constraint, energy storage equipment operation constraint and reliability constraint.
The EH planning configuration optimization double-layer model comprises an upper layer and a lower layer, wherein the upper layer solves 0-1 state variables to determine the configuration condition of equipment in the EH system, continuous variables are solved according to discrete variables on the upper layer, and the hourly output condition of the equipment is determined.
The upper layer is solved by adopting a quantum genetic algorithm, and iteration is carried out according to the lower layer solving result; and the lower layer is solved by a CPLEX solver.
The invention also provides an energy junction optimal configuration system, which comprises a memory and a processor;
the processor is configured to execute the computer program stored in the memory to implement the energy hub optimization configuration method of any one of claims 1-9.
The invention has the beneficial effects that: the influence problem of uncertainty of comprehensive demand response (IDR) at the user side can be fully considered, and the problem of random response of comprehensive demand is solved; the probability density function can be directly fitted according to historical data without depending on a parameter distribution form assumed in advance, and the method has higher applicability compared with the traditional parameter estimation and can give consideration to source-load bilateral seasonal correlation; the method can comprehensively consider two aspects of investment economy and operation economy, reduce the influence of uncertainty of output of user energy and intermittent power supply on a system configuration result while ensuring the optimal investment cost, and ensure the economic and environment-friendly operation of the system.
Drawings
FIG. 1 is a schematic flow diagram of one embodiment of the present invention;
FIG. 2 is a probability density distribution graph of historical data of power sources and loads in one embodiment of the invention;
FIG. 3 is a sample data diagram illustrating scene cuts in a process of creating a source-to-load bilateral typical day scene according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described below, but the scope of the claimed invention is not limited to the described.
Fig. 1 shows an energy hub optimization configuration method, which includes the following steps:
establishing a scene: establishing a source-load bilateral typical day scene based on historical data of a power source and a load;
modeling: adding an objective function and constraint conditions to establish an EH planning configuration optimization double-layer model based on a source-load bilateral typical daily scene;
solving: and solving the EH planning configuration optimization double-layer model by using a CPLEX solver to obtain an optimal configuration result and executing the optimal configuration result.
The historical data of the power source and the power load are wind speed data and power load data classified according to seasons.
The source-load bilateral typical daily scene is established by adopting the following steps:
establishing a function: establishing a wind speed-load joint probability distribution function in each time period based on a Copula method;
secondly, scene reduction: firstly, sampling a wind speed-load joint probability distribution function, carrying out inverse transformation on a sampling result to obtain a simulation data sequence with correlation, then carrying out inverse transformation to generate a scene tree, and carrying out scene reduction by adopting a rapid forward generation method to obtain a source-load bilateral typical daily scene.
In the step (r), a Frank Copula function is adopted as the Copula function.
The sampling of the wind speed-load joint probability distribution function is realized by adopting an Archimedes Copula function sampling method.
The objective function aims to minimize annual comprehensive operation cost, wherein the cost comprises annual installation cost CIN, operation maintenance cost COM, energy consumption cost CEC and emission cost CEM.
The constraint conditions comprise power balance constraint, purchased energy constraint, conventional equipment operation constraint, energy storage equipment operation constraint and reliability constraint.
The EH planning configuration optimization double-layer model comprises an upper layer and a lower layer, wherein the upper layer solves 0-1 state variables to determine the configuration condition of equipment in the EH system, continuous variables are solved according to discrete variables on the upper layer, and the hourly output condition of the equipment is determined.
The upper layer is solved by adopting a quantum genetic algorithm, and iteration is carried out according to the lower layer solving result; and the lower layer is solved by a CPLEX solver.
The invention also provides an energy junction optimal configuration system, which comprises a memory and a processor;
the processor is configured to execute the computer program stored in the memory to implement the energy hub optimization configuration method of any one of claims 1-9.
Example 1
By adopting the scheme, the process flow shown in figure 1 is specifically adopted as follows:
step 1: wind power output-baseline load uncertainty analysis
By combining the nonparametric kernel density estimation KDE method and the Copula theory, the uncertainty relation between the wind power output and the user baseline power load is described, and the seasonal relevance between the variable uncertainty data characteristic and the user baseline power load can be considered while the variable uncertainty data characteristic is described. The specific process is as follows:
(1) data preprocessing and sample correlation measure calculation
(ii) sorting historical data (in hours as sampling intervals) by season, for example as shown in FIG. 2, with spring xt i,yt iWind speed and load samples of the day i in the time t period of the season;
secondly, setting two-dimensional random vectors (X, Y) as binary variables of renewable energy output and electric load, (X)i,yi) Is the observed value in the sample. And calculating Kendall rank correlation coefficients tau of the two random variables, and judging whether a correlation relation exists or not. When the Kendall rank correlation coefficient τ ≠ 0, the correlation model of the binary random variable can be established using the following method.
(2) Determining the edge distribution by using a kernel density estimation method:
firstly, an optimal window width and a proper kernel function are selected, and since different kernel functions have little influence on an estimation result, a Gaussian function is selected as a kernel function of kernel density estimation.
② setting Xt i、Yt iSetting probability density functions of the wind speed and the load at t time as f for samples of the wind speed and the load at i day in t time period classified according to the seasonsWT(xt),fLOAD(yt) Then the kernel density estimates are respectively:
in the formula, n is the number of samples, sample data are classified according to seasons, and n is considered to be 90d in each season; h is the bandwidth; t is taken for 1-24 hours; k (u) is a kernel function,
(iii) separately kernel density estimation of probability density functionMeter
Integration, an edge distribution function F of the available variablesWT(xt),FLOAD(yt)。
(3) Relevance model construction based on Copula function
Firstly, determining an optimal Copula function, and calculating a Kendall rank correlation coefficient tau of the obtained wind speed and load
0, the two have weak positive correlation, so that a Frank Copula function which can give consideration to both non-negative and negative correlation of variables is selected to describe the correlation structure of the wind speed and the load;
secondly, estimating an unknown parameter theta in the Copula distribution function by adopting a maximum likelihood method, wherein the estimation formula is as follows:
wherein, M is the sample volume,
is an estimate of the unknown parameter θ in the Frank Copula function.
And thirdly, taking spring as an example, establishing a Copula theory-based wind speed-load joint probability distribution function for the edge distribution of two random variable time intervals in each time interval. Obtaining a wind speed-load joint probability distribution function in each time period as follows:
in the formula (I), the compound is shown in the specification,
when the Frank Copula function is selected as the Copula function for generating the primitive, the generating primitive is
(x)＝(―ln e―θ)―(ln e―θx―1)。
(4) The Copula function is sampled and scene subtraction is performed.
Firstly, according to the Archimedes Copula function sampling method, the joint probability distribution function of each time interval is sampled as shown in figure 3, and the edge distribution F is respectively distributed according to the wind speed and the loadWT(xt),FLOAD(yt) And then, carrying out inverse transformation on the generated random number vector to obtain a simulated wind speed and load sequence which meets the Copula function at each time interval and has correlation.
And secondly, performing inverse transformation on the sampling result to generate a scene tree. In order to reduce the calculation amount of model solution, a Fast Forward Reduction technology (Fast Forward Reduction) is adopted to carry out scene Reduction on an uncertain scene, and finally a source-load bilateral typical daily scene is obtained.
Compared with the uncertain scene generation method combining traditional parameter estimation and a Monte Carlo sampling method, the method is more suitable for the actual uncertain characteristics of the variables; meanwhile, the wind power output-load combined distribution constructed by the Copula theory reflects the seasonal relevance between the wind power output-load combined distribution and the Copula theory.
Step 2: integrated demand response uncertainty analysis
And (2) taking the power load uncertainty scene generated in the step (1) as a baseline load, and calculating the comprehensive demand random response based on the baseline load and the load elasticity coefficient double uncertainties as follows: in the multi-energy complementary EH system, the concept of time-of-use electricity price can be expanded to different types of energy prices, and price type IDR response quantity is calculated based on the multi-energy load elasticity coefficient. The demand for electricity in which a part of IDR cannot be replaced by other kinds of energy is called conventional electrical load; for the electricity demands of air conditioner heating, electric water heater heating and the like, a user can select to directly use EH for heat supply substitution by comparing the power supply price with the heat supply price in the peak electricity price period, the part of load is called as a substitutable load, and the response rates of two response modes are as follows:
in the formula ofe,t、εh,tElectrical load and thermal load elastic coefficients, respectively;
is the rate of change of electricity price;
representing the price difference between the electric energy and the heat energy in the t period;
for the consumers, the response amount is not always adjusted at will, but there is a certain fixed load ratio, and the general residential users should satisfy:
the IDR response can be expressed as a baseline load Le,tAnd response rate
The t-period IDR response is then:
in the above formula, s and t represent time, and are 1,2,3, … and 24; t → s represents the time t to transfer the electric load to the time s, and the response quantity of the conventional electric load is represented as the difference between the load transferred at the time and the load transferred at other times; since the heating price does not change with time, the alternative load in the period t is considered to be replaced by the heat load in the period.
In practice, the difference from the traditional price type DRSimilarly, the IDR uncertainty is derived from the following two aspects: uncertainty of load elastic coefficient and uncertainty of baseline load, baseline load Le,tThe uncertainty of (a) is described by the uncertainty scene generation method in the previous section; coefficient of elasticity epsilon influencing loade,t、εh,tIs more, and such parameters have less historical data, so their uncertainty is described using a positive distribution whose probability density function is:
in the formula, mu and sigma respectively represent the mean value and the standard deviation of the load elastic coefficient, and the mean value and the standard deviation can be updated according to the user load curve obtained according to the response condition after the mean value and the standard deviation are sampled by using a Latin hypercube sampling technology (LHS).
And step 3: establishing EH configuration and optimized operation double-layer model considering comprehensive demand response uncertainty
Step 3-1: objective function
The planning object of the invention mainly considers the configuration capacity and the configuration mode of components such as energy conversion and storage equipment, the planning design aim is to minimize the annual comprehensive operation cost of a hub, and the annual comprehensive operation cost considering uncertainty is determined by the annual installation cost CINOperation maintenance cost COMEnergy cost CECDischarge cost CEMSeveral components are formed, then the objective function is:
min f＝(CIN+COM+CEC+CEM) (9)
(1) full life cycle cost
The equipment initial installation investment cost can be reduced to the annual investment cost through the calculation of the equal-payment capital recovery:
in the formula, Pi,maxFor mounting of devices iAn amount; c. CIiInitial installation cost per unit capacity for device i; i isiTo determine the 0-1 variable of the installation state of the apparatus, IiWhen the equipment i is 1, the equipment i is equipment which is planned and installed by the planning strategy, otherwise, the equipment i is not installed; r is0M is the set planned operating age for the reference discount rate.
(2) Cost of operation and maintenance
k is the number of uncertain scenes; p is a radical ofucIs the probability of the occurrence of the scene uc,
the output power of the device i in the corresponding scene at the time t is obtained; c. CoiOperating maintenance costs per unit capacity of equipment.
(3) Cost of energy consumption
The difference between the expenditure of the hub for purchasing energy from the network and the profit of selling electricity from the hub to the network is taken as the energy consumption cost of the system and is expressed as:
in the formula (I), the compound is shown in the specification,
respectively representing the electricity purchase price, the gas purchase price and the electricity sale price at the time t;
and respectively the electricity purchasing power, the gas purchasing power and the electricity selling power at the moment t in the current scene.
(4) Cost of emissions
In the formula, piEMIs a CO2, SO2, NO2 unit capacity emission cost parameter matrix, EFNet,EFCHP,EFGBRespectively, online electricity purchasing, CHP and boiler emission factors;
respectively the power purchased by the network, the CHP and the GB generated energy.
Step 3-2: constraint conditions
The constraints to be considered when solving the objective function mainly include: energy conversion constraints, equipment output constraints, power/natural gas balance constraints, and the like.
(1) Power balance constraint
The system electricity, gas and heat subsystem bus power balance obtained based on the mathematical model of the energy hub is as follows:
in the formula
No power is supplied at time t; l ise,t(uc),Lh,tThe requirements of the user side on the electrical load and the thermal load are met in the period t;
for the time period t, the wind power generation output force,
the electric quantity is purchased on the internet for the time period t,
the power generation amount of the gas boiler is t period,
is the difference between the discharged amount and the charged amount of the battery in the period t,
to account for the amount of IDR response that is uncertain. The fixed natural gas load directly demanded by the customer is directly supplied from the natural gas network without passing through the energy hub primary coupling facility and is therefore not involved in configuration and optimization operation calculations. Formula source load uncertain scene variable
Le,t(uc) and IDR random response
And calculating by the second step.
(2) Energy purchase constraints
In the formula (I), the compound is shown in the specification,
respectively the upper power limits of the EH secondary electricity purchasing, gas purchasing and electricity selling;
the electricity purchases do not occur simultaneously, for a 0-1 variable that indicates whether electricity is purchased from the grid.
(3) Normal plant operating constraints
Operating capacity P of device it iShould be less than the installation capacity of the equipment
And is greater than the lowest allowable value
The CHP unit should meet the thermal power ratio constraint,
respectively the maximum and minimum thermoelectric ratios.
For power distribution network, gas turbine and gas boiler, the power variation needs to be between the ascending and descending constraint values of the slope
In the meantime.
A multi-energy system containing high permeability renewable energy sources requires reserve spinning reserve capacity in an optimized configuration to cope with extreme conditions, where,
upper limit of power supplied to each apparatus, RtIs the total spare capacity of the system.
(4) Energy storage device operational constraints
The energy storage device is constrained by the capacity of the energy storage device and the charge and discharge capacity, taking the electric energy storage as an example: t moment electric energy storage charging and discharging capacity
Is the amount of charge
And the amount of discharge
A difference value;
the charging and discharging do not occur simultaneously for 0-1 variable representing whether the energy storage battery is charged or discharged at the moment t; residual capacity of energy storage device
Should be less than the amount of electricity when the stored energy is full
And is greater than the minimum residual capacity
The specific constraints are as follows:
similarly, the thermal energy storage should also satisfy the above constraint relationship.
(5) Reliability constraints
Setting an Equivalent Loss Factor (ELF) to represent the reliability of the system, wherein the ELF is defined as the annual non-supplied electric quantity
And the required electric quantity Le,t(uc) the ratio of the amount of electricity not supplied should be less than the maximum allowable loss factor ELFmax。
And 4, step 4: determining a two-layer model solution strategy
The specific method for solving the EH planning configuration and optimization operation problem double-layer model comprises the following steps: because the product of the binary discrete variable and the continuous variable exists in the constraint condition, the programming problem becomes an MINLP (mixed integer nonlinear programming) problem, and the MINLP problem is decomposed into a two-layer problem solving method: solving 0-1 state variables at the upper layer, determining the equipment configuration condition in the EH system, and increasing the possibility of chromosome change by using a quantum genetic algorithm combining quantum computation and a genetic algorithm in order to avoid the problem that the discrete variables processed by the traditional genetic algorithm are easy to fall into the local optimal solution; and the lower layer solves continuous variables according to the upper discrete variables, determines the hourly output condition of the equipment, returns the solved result to the upper layer, generates a new generation of upper variable population through the genetic variation of the quantum revolving door, and continuously iterates the upper and lower results until a global optimal solution is obtained. In order to ensure the calculation efficiency, the lower layer is solved by using a CPLEX solver. The CPLEX solver may be invoked from the YALMIP toolbox of the MATLAB environment, or from GAMS software, or from other libraries or toolboxes. Before the model is solved, firstly, a source-charge bilateral uncertainty scene is generated, an electrical load curve is updated according to IDR response quantity, and the updated source-charge bilateral curve is used as the input of the model. Therefore, the EH planning problem considering the uncertainty of the IDR response quantity is converted into a double-layer planning model, the upper layer determines the configuration condition of the EH equipment, and the lower layer determines the optimized operation energy distribution condition of the day.
And 5: determining examples
And (3) carrying out simulation analysis on the calculation example by adopting a CPLEX solver, and verifying the effectiveness and the rationality of the provided model.
Verification example 1
Four schemes were chosen for comparative analysis, among which:
in the first scheme, a traditional energy supply mode is adopted, electricity and heat requirements are supplied by a power distribution network and a gas boiler respectively, and uncertainty and demand response are not considered;
the second scheme, the third scheme and the fourth scheme adopt an energy hub operation mode of integrated new energy power generation, power is supplied according to the priority combination of wind power generation, a CHP unit, electric energy storage and a power distribution network, the CHP unit and a gas boiler supply heat, and the second scheme only considers the uncertainty of wind power generation and adopts an energy hub operation mode of integrated new energy power generation to supply power;
in the third scheme, power supply is realized by only considering uncertainty of a power utilization side and adopting an energy hub operation mode of integrating new energy power generation;
and the fourth scheme adopts the scheme, comprehensively considers the uncertainty of the supply and demand double sides and the uncertainty of the comprehensive demand response of heat/electricity, and adopts an energy hub operation mode of integrating new energy power generation for power supply.
And (3) running calculation on a computer with Intel Core i5-3230M CPU @2.60GHz and a memory of 4GB, and substituting the generated uncertainty scene into four schemes for solving, wherein the system configuration result corresponding to each scheme is obtained and is shown in table 1, and the required running cost is shown in table 2.
TABLE 1 Equipment selection and Capacity Allocation results
TABLE 2 plan cost comparison
It can be seen from table 1 and table 2 that in the first scheme, a conventional energy supply mode is adopted, and a large-capacity gas boiler and a transformer are required to be installed to meet the requirements of heat load and electric load without energy conversion equipment, so that the investment cost is lowest, and renewable energy sources are not considered for energy supply, so that the energy consumption cost and the emission cost of the system are highest. Considering that the strong uncertainty influence of the wind power side is stabilized, the number of the electric energy storage and heat energy storage units required by the scheme two is the largest, meanwhile, a high-capacity CHP unit is required to ensure the energy supply quality, the unit capacity investment of the CHP unit is high, and therefore the investment cost of the scheme two is the highest; the uncertainty of the user's ability habit to compare with the change of the wind speed is smaller, so the spare capacity of the equipment required to be put into the scheme III is reduced, the number of the electric energy storage and the heat energy storage is reduced, and the system investment cost is reduced by 30.6 percent compared with the scheme II; the uncertainty of the wind power strength enables the electricity purchasing quantity needed by the gentle net load curve of the scheme II to be more, so that the energy consumption cost of the scheme III is reduced by 17.7% compared with the scheme II, and the emission cost is lower; the uncertainty of source charge and double sides is restrained to a certain extent by adding the comprehensive demand response behavior of the four user sides of the scheme, the dependence on the capacity of the CHP unit is reduced, the investment cost is reduced by a small margin compared with that of the scheme two, the electricity purchasing quantity of the scheme four is reduced by an electricity load curve with smooth comprehensive demand response, and the operation cost is reduced by 9.74 percent compared with that of the scheme two; compared with the third scheme, the uncertainty of the supply and demand double sides and the uncertainty of the heat/electricity comprehensive demand response are comprehensively considered, and the method is closer to the actual working condition.
Analyzing the influence of the IDR response behavior fluctuation degree of the user on the pivot economy, and adjusting the load elastic coefficient epsilone,t、εh,tObtaining the annual comprehensive operation cost variation trend of the energy hub by the sampling variance: the load elasticity coefficient is influenced by various factors such as user's will, policy guidance and the like, so that epsilon is reduced within a certain rangee,tThe uncertainty of user participation in demand response is reduced, the standby capacity of equipment required by the system is reduced, and the comprehensive operation cost of the hub year is obviously reduced along with the reduction of the sampling variance; coefficient of thermal load elasticity εh,tThe influence of sampling variance change on annual comprehensive operation cost is small, and only when epsilone,tWhen variance is large, it follows epsilonh,tThe sampling variance is increased, and the comprehensive cost is reduced in a small range; when epsilone,tThe sampling variance of (c) continues to decrease, enlarging epsilonh,tThe variance and the comprehensive operation cost of the hub have small change range, and the main reason is that epsilone,tWhen the load capacity of the system is small, the load capacity participating in IDR is basically stable, compared with the benefit brought to the system by reducing the spare capacity of the equipment, the peak clipping and valley filling benefit generated by the heat energy substitution response mode is small, and the comprehensive economic cost of the system cannot be changed remarkably.
Therefore, the source-charge bilateral uncertainty probability model is described by using nonparametric density estimation and a Copula theory, and the defect that the traditional source-charge bilateral uncertainty model is difficult to accurately fit actual parameters by using parameter estimation is overcome; calculation shows that seasonal relativity exists on both sides of the source load, and the seasonal relativity on both sides of the source load can be considered by combining the Copula theory.
And secondly, based on the double uncertainties of the baseline load and the load elastic coefficient, providing a calculation method of the electric/thermal comprehensive demand random response quantity. And (4) obtaining a source load uncertainty model on the basis of discussing IDR response quantity certainty. And finally, carrying out simulation test on the example, wherein a simulation result shows that the annual operation cost of the system is obviously increased under the condition of only considering the uncertainty of the power generation side of the renewable energy source and the load side of the user, but the wind power output and the load fluctuation can be restrained to a certain extent by considering the comprehensive consideration of the electricity/heat IDR response quantity, the net load curve is stabilized, and the operation and emission cost is reduced at the same time. Therefore, the invention is obviously beneficial to improving the economical efficiency and environmental protection of the system.
Claims (10)
1. An energy hub optimal configuration method is characterized in that: the method comprises the following steps:
establishing a scene: establishing a source-load bilateral typical day scene based on historical data of a power source and a load;
modeling: adding an objective function and constraint conditions to establish an EH planning configuration optimization double-layer model based on a source-load bilateral typical daily scene;
solving: and solving the EH planning configuration optimization double-layer model by using a CPLEX solver to obtain an optimal configuration result and executing the optimal configuration result.
2. The energy hub optimal configuration method of claim 1, wherein: the historical data of the power source and the power load are wind speed data and power load data classified according to seasons.
3. The energy hub optimal configuration method of claim 1, wherein: the source-load bilateral typical daily scene is established by adopting the following steps:
establishing a function: establishing a wind speed-load joint probability distribution function in each time period based on a Copula method;
secondly, scene reduction: firstly, sampling a wind speed-load joint probability distribution function, carrying out inverse transformation on a sampling result to obtain a simulation data sequence with correlation, then carrying out inverse transformation to generate a scene tree, and carrying out scene reduction by adopting a rapid forward generation method to obtain a source-load bilateral typical daily scene.
4. The energy hub optimization configuration method of claim 3, wherein: in the step (r), a Frank Copula function is adopted as the Copula function.
5. The energy hub optimization configuration method of claim 3, wherein: the sampling of the wind speed-load joint probability distribution function adopts an Archimedes Copula function sampling method.
6. The energy hub optimal configuration method of claim 1, wherein: the objective function is targeted to minimize annual combined operating costs, including annual installation cost CINOperation maintenance cost COMEnergy cost CECAnd emission cost CEM。
7. The energy hub optimal configuration method of claim 1, wherein: the constraints include power balance constraints, commercially available energy constraints, normal equipment operating constraints, energy storage equipment operating constraints, and reliability constraints.
8. The energy hub optimal configuration method of claim 1, wherein: the EH planning configuration optimization double-layer model comprises an upper layer and a lower layer, wherein the upper layer solves 0-1 state variables to determine the configuration condition of equipment in the EH system, continuous variables are solved according to discrete variables on the upper layer, and the hour output condition of the equipment is determined.
9. The energy hub optimal configuration method of claim 1, wherein: the upper layer is solved by adopting a quantum genetic algorithm, and iteration is carried out according to the lower layer solving result; and the lower layer is solved by adopting a CPLEX solver.
10. An energy hub optimal configuration system, characterized by: comprising a memory and a processor;
the processor is configured to execute a computer program stored in the memory to implement the energy hub optimization configuration method of any one of claims 1-9.
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